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Generative Adversarial Networks With Help of Deep Learning Simplified

Generative Adversarial Networks is a class of machine  learning frameworks. They are algorithmic architecture and deep generative models that composed two neural networks. They use in video, image and voice generation. With the help of this notes you will be able to see real Examples applying generative adversarial network with help of Deep Learning.we   are sharing this advance Notes with you guys you can download freely 

Note : this Notes is only for Study Purpose 

What is Deep Learning?

Deep Learning is a class of machine learning and artificial intelligence that imitate the way human gain certain type of knowledge. Deep learning allows machines to solve complex problem when using data set. 

In this PDF notes you’ll learn basic concept of generative network to generate practical image from unlabeled data. By this notes you’ll learn how to solve real-world problems and make high quality image generation. In this PDF notes you will learn how deep learning work in semi supervised domain. This notes is very useful and helpful for researchers, developers and students. 

In this notes you’ll learn that how to combine images from text. It is very useful and helpful notes. You can download this PDF notes free from here.

Notes Generative Adversarial Networks With Help of Deep Learning
Language  English
Note Type PDF
Size 10 MB
For  Fresher/Intermediate 

You Cover These Topics:

  • Introduction to Deep Learning

Evolution of Deep Learning

Learning Rate Tuning


Deep Neural Network

  • Unsupervised Learning with GAN

Automating Human Task with Deep Neural Networks

Implementation of GAN

The Building Bocks of GAN

Applications of GAN

The Purpose of GAN

  • Transfer Image Style Across Various Domains

Bridging the Gap Between Supervised and Unsupervised Learning

Introduction to Conditional GAN

The Training Procedure of BEGAN

Architecture of BEGAN

  • Building Realistic Image From Your Text 

Introduction to StackGAN

Conditional Augmentation

Architecture Details of StackGAN

Implementation of DiscoGAN

DiscoGAN Versus CycleGAN

  • Using Various Generative Models to Generate Image

Introduction to Transfer Learning

The Purpose of Transfer Learning

Large Scale Deep Learning with Apache Spark

Architecture of the SRGAN

  • Taking Machine Learning to Production

Building an Image Correction System Using DCGAN

Step for Building Image Correction System

Micro service Architecture Using Containers

Benefits of Using Containers



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